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A two dimensional counterpart of silicon designated as Silicene has opened new perspectives for applications in various fields. Colloidal suspension of freestanding Silicene has been synthesized, which can easily be deposited on any kind of substrate. Room temperature photoluminescence study along with various structural and morphological characterizations have been performed on synthesized colloidal Silicene flakes. Obtained photoluminescence dataset has a complex relationship between wavelength & photoluminescence intensity, Machine Learning models including Random Forest and Gradient Boosting have been adopted in order to capture these patterns more accurately than simple formulas.

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Optimization and Comparison of Machine Learning Models for Predictive Modeling of Photoluminescence Spectrum of Freestanding Colloidal Silicene Ink: A Multi-factor Performance Evaluation Framework

  • 会议论文

  • 发表日期：

    2026年

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  • Susmita Biswas,
  • Siddhartha Roy,
  • Rangon Sarkar,
  • Raisa Santra

摘要

A two dimensional counterpart of silicon designated as Silicene has opened new perspectives for applications in various fields. Colloidal suspension of freestanding Silicene has been synthesized, which can easily be deposited on any kind of substrate. Room temperature photoluminescence study along with various structural and morphological characterizations have been performed on synthesized colloidal Silicene flakes. Obtained photoluminescence dataset has a complex relationship between wavelength & photoluminescence intensity, Machine Learning models including Random Forest and Gradient Boosting have been adopted in order to capture these patterns more accurately than simple formulas.